Real-time Data Ingestion

SUMMARY

Real-time data ingestion is the continuous process of collecting, processing, and loading data into a database system as it is generated. This approach enables organizations to analyze and act on data within milliseconds of its creation, making it crucial for time-sensitive applications like financial trading, industrial monitoring, and IoT systems.

How real-time ingestion works

Real-time ingestion systems typically follow a multi-stage pipeline:

Data capture from source systems
Optional preprocessing and transformation
Immediate writing to the target database

Unlike batch ingestion, real-time ingestion processes each record or small group of records immediately, without waiting to accumulate larger batches.

Next generation time-series database

QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.

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Key requirements for real-time ingestion

High throughput capacity

Systems must handle massive volumes of incoming data without introducing delays:

SELECT count()
FROM trades
SAMPLE BY 1s
WHERE timestamp > dateadd('h', -1, now());

This query demonstrates how systems track ingestion rates over time to ensure performance meets requirements.

Low latency processing

Real-time ingestion systems must minimize the time between data creation and availability:

Network transport optimization
Efficient write buffering
Optimized storage engine design

Data quality controls

Systems typically implement real-time validation:

Schema compliance
Data type verification
Business rule validation
Deduplication checks

Next generation time-series database

Try live demo Read documentation

Common challenges and solutions

Out-of-order data handling

Real-time systems must handle data arriving in non-chronological order:

SELECT symbol, price, timestamp
FROM trades
WHERE timestamp > dateadd('m', -5, now())
ORDER BY timestamp;

Backpressure management

When ingestion rates exceed processing capacity, systems need mechanisms to handle the overflow:

Buffer management
Rate limiting
Load shedding

High availability requirements

Real-time ingestion systems often require:

Redundant ingestion paths
Failover mechanisms
Data consistency guarantees

Applications and use cases

Financial markets

Market data feeds
Trade execution systems
Risk monitoring

Industrial IoT

Sensor data collection
Equipment monitoring
Process control systems

Real-time analytics

User behavior tracking
Performance monitoring
Anomaly detection

The success of real-time ingestion systems often depends on their ability to handle these challenges while maintaining consistent performance and data quality. Organizations must carefully design their ingestion architecture to match their specific requirements for latency, throughput, and data consistency.